Ocular inflammatory diseases, including uveitis and AMD, cause significant visual loss. Using a variety of immune techniques we have evaluated various characteristics of immune cells in these diseases, including signaling pathways, e.g. inflammatory and autoimmune pathway. We have seen varying molecular signatures for uveitis. Of particular interest is the identification of IL-22. The expression of IL-22 has been recently associated with Th17 cells which is elevated in the serum of AMD and uveitis patients. We have shown that IL-22 resulted in apoptosis in cultured primary RPE cells, possibly by decreasing the phosphorylated-Bad level. In addition, we saw increased IL-17 activity in the immune cells of patients with age related macular degeneration. We have reported epigenetic alterations the immune cells of AMD patients but this remains to be verified. However, an increase in the presence of immune markers such as IL-17 and its receptor in the eyes of AMD were noted, whatever their mechanism may be. In addition, patients with steroid refractory uveitis have a characteristic subpopulation of steroid refractory CD4+ T cells in their peripheral blood. Previously studies have demonstrated that this steroid refractory phenotype is restricted to the central memory pool of CD4+ cells which have the capacity to generate IL-17. We therefore compared transcriptomic responses of Th1 and Th17 cells to corticosteroids in order to identify novel biomarkers and targets for therapeutic intervention in steroid refractory disease. Steroid refractory patients have a greater propensity than sensitive patients to generate Th17 cells, but Th17 cells from either group of patients have a similarly restricted change in gene expression following exposure to Dex compared with Th1 cells. Using additional techniques we have identified that a subgroup of uveitis patients have markedly shortened telomere length. In addition we have noted circulating IL-17 in sarcoidosis patients which is associated with active disease. We have collaborated with the University of Maryland to look at new ways to identify disease phenotypes. One particularly interesting approach has been that of supercell statistics, a single cell based averaging procedure followed by a machine learning scheme.